# Touvigation: Embodied Adaptive Object Acquisition for Blind and Low-Vision Users in Unfamiliar Indoor Environments

arXiv: https://arxiv.org/abs/2609.21828 · HTML: https://mlsanity.com/paper/2609.21828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 18 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark](https://mlsanity.com/paper/2412.07825/) (2024)
- [WorldScribe: Towards Context-Aware Live Visual Descriptions](https://mlsanity.com/paper/2408.06627/) (2024)
- [Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People](https://mlsanity.com/paper/2403.15604/) (2024)
- [Vision Skills Needed to Answer Visual Questions](https://mlsanity.com/paper/2010.03160/) (2020)
- [Misfitting With AI: How Blind People Verify and Contest AI Errors](https://mlsanity.com/paper/2408.06546/) (2024)
- [Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System](https://mlsanity.com/paper/2410.04005/) (2024)
- [Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models](https://mlsanity.com/paper/2502.16098/) (2025)
- [Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision People](https://mlsanity.com/paper/2503.05899/) (2025)
- [WanderGuide: Indoor Map-less Robotic Guide for Exploration by Blind People](https://mlsanity.com/paper/2502.08906/) (2025)
- [StreetNav: Leveraging Street Cameras to Support Precise Outdoor Navigation for Blind Pedestrians](https://mlsanity.com/paper/2310.00491/) (2023)
- [TouchScribe: Augmenting Non-Visual Hand-Object Interactions with Automated Live Visual Descriptions](https://mlsanity.com/paper/2602.07802/) (2026)
- [How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People](https://mlsanity.com/paper/2602.13469/) (2026)
- [NaviSense: A Multimodal Assistive Mobile application for Object Retrieval by Persons with Visual Impairment](https://mlsanity.com/paper/2509.18672/) (2025)
- [Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users](https://mlsanity.com/paper/2602.16930/) (2026)
- [DuoZone: A User-Centric, LLM-Guided Mixed-Initiative XR Window Management System](https://mlsanity.com/paper/2511.15676/) (2025)
- [Feelium: A Touchable Blimp Body for Aerial Telepresence](https://mlsanity.com/paper/2608.29391/) (2026)

## Abstract (arXiv)

Blind and low-vision users often face challenges when locating and physically acquiring objects in unfamiliar indoor environments. Existing vision-language-model-based assistants can provide semantic descriptions but may introduce latency, hallucinations, and guidance that is poorly aligned with embodied action. We present Touvigation, a hands-free object acquisition system that combines vision-language understanding with persistent local spatial modeling to provide low-latency, body-relative guidance. Drawing on formative interviews with eight blind and low-vision participants, we design a multi-stage guidance framework that adapts spatial references as users transition from orienting, to walking, to reaching and tactile verification. We evaluated Touvigation with 12 blind and low-vision participants against a multimodal large-language-model assistant and unassisted search. Touvigation achieved 100% task success, compared with 58% for the multimodal assistant and 85% for unassisted search, while reducing completion time and cognitive workload. Our findings demonstrate how persistent spatial grounding and adaptive embodied guidance can improve object acquisition for blind and low-vision users.